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st: Several questions regarding xtprobit and margins command

From   TMorville <>
Subject   st: Several questions regarding xtprobit and margins command
Date   Tue, 6 Nov 2012 07:43:51 -0800 (PST)

Hi everyone, my first post at StataList, so be nice :)

I have a set of questions regarding the margins command, and marginal
effects in general.

I have a unbalanced paneldataset of 4124 observations, unevenly distributed
on 18 subjects. 

My model is as follows: P(stop) = Outcome outcome_lag1 seqEarn, which im
estimating in a RE probit setting with xtprobit command.

Outcome: Outcome of a dice in period t. Lies from 1 to 6
Outcome_lag1: Outcome of the dice in period t-1
seqEarn: Accumulated earnings over each game. Drops to 0 if subject chooses
to stop, or the dice shows a one. Starts at zero, and can only get more
positive as people climb the reward ladder.

All of these regressors are significant:


Sooo, now the questions begin:

1) If i use -margins Outcome- (followed this guide, then i get this errormessage:
"'Outcome' not found in list of covariates", and that actually is the case
for all margins commands, and is my number one headache.

The only marginscommand that works, is if i use the -margins, dydx(Outcome
outcome_lag1 seqEarn)-, which leads me to my next problem:

2) When i use a -margins, dydx(Outcome outcome_lag1 seqEarn)- my marginal
effects are exactly the same as my regression coefficients?
If i change the code to -margins, dydx(Outcome outcome_lag1 seqEarn)
atmeans- they're the same again..?

Im really confused about this, and i've read the ealier post
(, which covers
some of the questions i have, but dosen't really anwser them.

If i use mfx compute, predict(pu0) they change, but they become very small.
And im guess that pu0 means that we set the randomeffectsslope to the same
for all subjects, which is a bad idea for my data..

3) If i choose to ignore the fact that my marginal effects are the same as
my probit regression coefficients, then im in my next pickle. That my
marginal effects are constant. If i plot the predicted probability of
stopping the game, over seqEarn, its constant, which suits my data very
badly. And im afraid that i've misunderstood something very basic.

What i would idealy like to see, is that predicted probability changes both
over seqEarn and over Outcome and outcome_lag1 in some systematic way, but
right now my newbieness in Stata problemshooting is driving me up the wall.


Background (just for the interested):

I'm currently working with a dataset of 18 subjects, playing a virtual
dicegame for 25 mins while in a fMRI scanner. The dice is random (1-6), and
if you roll one, you lose whatever you accumulated this round. It's a
balloon kind of thing: How far do people dare to go up the exponential
reward ladder, before banking their earnings.

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